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  <h1>Source code for torch.nn.modules.instancenorm</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">.batchnorm</span> <span class="kn">import</span> <span class="n">_NormBase</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>


<span class="k">class</span> <span class="nc">_InstanceNorm</span><span class="p">(</span><span class="n">_NormBase</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_features</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                 <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">_InstanceNorm</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
            <span class="n">num_features</span><span class="p">,</span> <span class="n">eps</span><span class="p">,</span> <span class="n">momentum</span><span class="p">,</span> <span class="n">affine</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span>

    <span class="k">def</span> <span class="nf">_load_from_state_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">local_metadata</span><span class="p">,</span> <span class="n">strict</span><span class="p">,</span>
                              <span class="n">missing_keys</span><span class="p">,</span> <span class="n">unexpected_keys</span><span class="p">,</span> <span class="n">error_msgs</span><span class="p">):</span>
        <span class="n">version</span> <span class="o">=</span> <span class="n">local_metadata</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;version&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
        <span class="c1"># at version 1: removed running_mean and running_var when</span>
        <span class="c1"># track_running_stats=False (default)</span>
        <span class="k">if</span> <span class="n">version</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">track_running_stats</span><span class="p">:</span>
            <span class="n">running_stats_keys</span> <span class="o">=</span> <span class="p">[]</span>
            <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;running_mean&#39;</span><span class="p">,</span> <span class="s1">&#39;running_var&#39;</span><span class="p">):</span>
                <span class="n">key</span> <span class="o">=</span> <span class="n">prefix</span> <span class="o">+</span> <span class="n">name</span>
                <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">state_dict</span><span class="p">:</span>
                    <span class="n">running_stats_keys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">running_stats_keys</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">error_msgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
                    <span class="s1">&#39;Unexpected running stats buffer(s) </span><span class="si">{names}</span><span class="s1"> for </span><span class="si">{klass}</span><span class="s1"> &#39;</span>
                    <span class="s1">&#39;with track_running_stats=False. If state_dict is a &#39;</span>
                    <span class="s1">&#39;checkpoint saved before 0.4.0, this may be expected &#39;</span>
                    <span class="s1">&#39;because </span><span class="si">{klass}</span><span class="s1"> does not track running stats by default &#39;</span>
                    <span class="s1">&#39;since 0.4.0. Please remove these keys from state_dict. If &#39;</span>
                    <span class="s1">&#39;the running stats are actually needed, instead set &#39;</span>
                    <span class="s1">&#39;track_running_stats=True in </span><span class="si">{klass}</span><span class="s1"> to enable them. See &#39;</span>
                    <span class="s1">&#39;the documentation of </span><span class="si">{klass}</span><span class="s1"> for details.&#39;</span>
                    <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">names</span><span class="o">=</span><span class="s2">&quot; and &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s1">&#39;&quot;</span><span class="si">{}</span><span class="s1">&quot;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">running_stats_keys</span><span class="p">),</span>
                            <span class="n">klass</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">running_stats_keys</span><span class="p">:</span>
                    <span class="n">state_dict</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>

        <span class="nb">super</span><span class="p">(</span><span class="n">_InstanceNorm</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">_load_from_state_dict</span><span class="p">(</span>
            <span class="n">state_dict</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">local_metadata</span><span class="p">,</span> <span class="n">strict</span><span class="p">,</span>
            <span class="n">missing_keys</span><span class="p">,</span> <span class="n">unexpected_keys</span><span class="p">,</span> <span class="n">error_msgs</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_check_input_dim</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">instance_norm</span><span class="p">(</span>
            <span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">running_var</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="ow">or</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">track_running_stats</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>


<div class="viewcode-block" id="InstanceNorm1d"><a class="viewcode-back" href="../../../../nn.html#torch.nn.InstanceNorm1d">[docs]</a><span class="k">class</span> <span class="nc">InstanceNorm1d</span><span class="p">(</span><span class="n">_InstanceNorm</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Applies Instance Normalization over a 3D input (a mini-batch of 1D</span>
<span class="sd">    inputs with optional additional channel dimension) as described in the paper</span>
<span class="sd">    `Instance Normalization: The Missing Ingredient for Fast Stylization`_ .</span>

<span class="sd">    .. math::</span>

<span class="sd">        y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta</span>

<span class="sd">    The mean and standard-deviation are calculated per-dimension separately</span>
<span class="sd">    for each object in a mini-batch. :math:`\gamma` and :math:`\beta` are learnable parameter vectors</span>
<span class="sd">    of size `C` (where `C` is the input size) if :attr:`affine` is ``True``.</span>

<span class="sd">    By default, this layer uses instance statistics computed from input data in</span>
<span class="sd">    both training and evaluation modes.</span>

<span class="sd">    If :attr:`track_running_stats` is set to ``True``, during training this</span>
<span class="sd">    layer keeps running estimates of its computed mean and variance, which are</span>
<span class="sd">    then used for normalization during evaluation. The running estimates are</span>
<span class="sd">    kept with a default :attr:`momentum` of 0.1.</span>

<span class="sd">    .. note::</span>
<span class="sd">        This :attr:`momentum` argument is different from one used in optimizer</span>
<span class="sd">        classes and the conventional notion of momentum. Mathematically, the</span>
<span class="sd">        update rule for running statistics here is</span>
<span class="sd">        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`,</span>
<span class="sd">        where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the</span>
<span class="sd">        new observed value.</span>

<span class="sd">    .. note::</span>
<span class="sd">        :class:`InstanceNorm1d` and :class:`LayerNorm` are very similar, but</span>
<span class="sd">        have some subtle differences. :class:`InstanceNorm1d` is applied</span>
<span class="sd">        on each channel of channeled data like multidimensional time series, but</span>
<span class="sd">        :class:`LayerNorm` is usually applied on entire sample and often in NLP</span>
<span class="sd">        tasks. Additionally, :class:`LayerNorm` applies elementwise affine</span>
<span class="sd">        transform, while :class:`InstanceNorm1d` usually don&#39;t apply affine</span>
<span class="sd">        transform.</span>

<span class="sd">    Args:</span>
<span class="sd">        num_features: :math:`C` from an expected input of size</span>
<span class="sd">            :math:`(N, C, L)` or :math:`L` from input of size :math:`(N, L)`</span>
<span class="sd">        eps: a value added to the denominator for numerical stability. Default: 1e-5</span>
<span class="sd">        momentum: the value used for the running_mean and running_var computation. Default: 0.1</span>
<span class="sd">        affine: a boolean value that when set to ``True``, this module has</span>
<span class="sd">            learnable affine parameters, initialized the same way as done for batch normalization.</span>
<span class="sd">            Default: ``False``.</span>
<span class="sd">        track_running_stats: a boolean value that when set to ``True``, this</span>
<span class="sd">            module tracks the running mean and variance, and when set to ``False``,</span>
<span class="sd">            this module does not track such statistics and always uses batch</span>
<span class="sd">            statistics in both training and eval modes. Default: ``False``</span>

<span class="sd">    Shape:</span>
<span class="sd">        - Input: :math:`(N, C, L)`</span>
<span class="sd">        - Output: :math:`(N, C, L)` (same shape as input)</span>

<span class="sd">    Examples::</span>

<span class="sd">        &gt;&gt;&gt; # Without Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.InstanceNorm1d(100)</span>
<span class="sd">        &gt;&gt;&gt; # With Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.InstanceNorm1d(100, affine=True)</span>
<span class="sd">        &gt;&gt;&gt; input = torch.randn(20, 100, 40)</span>
<span class="sd">        &gt;&gt;&gt; output = m(input)</span>

<span class="sd">    .. _`Instance Normalization: The Missing Ingredient for Fast Stylization`:</span>
<span class="sd">        https://arxiv.org/abs/1607.08022</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s1">&#39;InstanceNorm1d returns 0-filled tensor to 2D tensor.&#39;</span>
                <span class="s1">&#39;This is because InstanceNorm1d reshapes inputs to&#39;</span>
                <span class="s1">&#39;(1, N * C, ...) from (N, C,...) and this makes&#39;</span>
                <span class="s1">&#39;variances 0.&#39;</span>
            <span class="p">)</span>
        <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected 3D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span></div>


<div class="viewcode-block" id="InstanceNorm2d"><a class="viewcode-back" href="../../../../nn.html#torch.nn.InstanceNorm2d">[docs]</a><span class="k">class</span> <span class="nc">InstanceNorm2d</span><span class="p">(</span><span class="n">_InstanceNorm</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs</span>
<span class="sd">    with additional channel dimension) as described in the paper</span>
<span class="sd">    `Instance Normalization: The Missing Ingredient for Fast Stylization`_ .</span>

<span class="sd">    .. math::</span>

<span class="sd">        y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta</span>

<span class="sd">    The mean and standard-deviation are calculated per-dimension separately</span>
<span class="sd">    for each object in a mini-batch. :math:`\gamma` and :math:`\beta` are learnable parameter vectors</span>
<span class="sd">    of size `C` (where `C` is the input size) if :attr:`affine` is ``True``.</span>

<span class="sd">    By default, this layer uses instance statistics computed from input data in</span>
<span class="sd">    both training and evaluation modes.</span>

<span class="sd">    If :attr:`track_running_stats` is set to ``True``, during training this</span>
<span class="sd">    layer keeps running estimates of its computed mean and variance, which are</span>
<span class="sd">    then used for normalization during evaluation. The running estimates are</span>
<span class="sd">    kept with a default :attr:`momentum` of 0.1.</span>

<span class="sd">    .. note::</span>
<span class="sd">        This :attr:`momentum` argument is different from one used in optimizer</span>
<span class="sd">        classes and the conventional notion of momentum. Mathematically, the</span>
<span class="sd">        update rule for running statistics here is</span>
<span class="sd">        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`,</span>
<span class="sd">        where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the</span>
<span class="sd">        new observed value.</span>

<span class="sd">    .. note::</span>
<span class="sd">        :class:`InstanceNorm2d` and :class:`LayerNorm` are very similar, but</span>
<span class="sd">        have some subtle differences. :class:`InstanceNorm2d` is applied</span>
<span class="sd">        on each channel of channeled data like RGB images, but</span>
<span class="sd">        :class:`LayerNorm` is usually applied on entire sample and often in NLP</span>
<span class="sd">        tasks. Additionally, :class:`LayerNorm` applies elementwise affine</span>
<span class="sd">        transform, while :class:`InstanceNorm2d` usually don&#39;t apply affine</span>
<span class="sd">        transform.</span>

<span class="sd">    Args:</span>
<span class="sd">        num_features: :math:`C` from an expected input of size</span>
<span class="sd">            :math:`(N, C, H, W)`</span>
<span class="sd">        eps: a value added to the denominator for numerical stability. Default: 1e-5</span>
<span class="sd">        momentum: the value used for the running_mean and running_var computation. Default: 0.1</span>
<span class="sd">        affine: a boolean value that when set to ``True``, this module has</span>
<span class="sd">            learnable affine parameters, initialized the same way as done for batch normalization.</span>
<span class="sd">            Default: ``False``.</span>
<span class="sd">        track_running_stats: a boolean value that when set to ``True``, this</span>
<span class="sd">            module tracks the running mean and variance, and when set to ``False``,</span>
<span class="sd">            this module does not track such statistics and always uses batch</span>
<span class="sd">            statistics in both training and eval modes. Default: ``False``</span>

<span class="sd">    Shape:</span>
<span class="sd">        - Input: :math:`(N, C, H, W)`</span>
<span class="sd">        - Output: :math:`(N, C, H, W)` (same shape as input)</span>

<span class="sd">    Examples::</span>

<span class="sd">        &gt;&gt;&gt; # Without Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.InstanceNorm2d(100)</span>
<span class="sd">        &gt;&gt;&gt; # With Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.InstanceNorm2d(100, affine=True)</span>
<span class="sd">        &gt;&gt;&gt; input = torch.randn(20, 100, 35, 45)</span>
<span class="sd">        &gt;&gt;&gt; output = m(input)</span>

<span class="sd">    .. _`Instance Normalization: The Missing Ingredient for Fast Stylization`:</span>
<span class="sd">        https://arxiv.org/abs/1607.08022</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">4</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected 4D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span></div>


<div class="viewcode-block" id="InstanceNorm3d"><a class="viewcode-back" href="../../../../nn.html#torch.nn.InstanceNorm3d">[docs]</a><span class="k">class</span> <span class="nc">InstanceNorm3d</span><span class="p">(</span><span class="n">_InstanceNorm</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs</span>
<span class="sd">    with additional channel dimension) as described in the paper</span>
<span class="sd">    `Instance Normalization: The Missing Ingredient for Fast Stylization`_ .</span>

<span class="sd">    .. math::</span>

<span class="sd">        y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta</span>

<span class="sd">    The mean and standard-deviation are calculated per-dimension separately</span>
<span class="sd">    for each object in a mini-batch. :math:`\gamma` and :math:`\beta` are learnable parameter vectors</span>
<span class="sd">    of size C (where C is the input size) if :attr:`affine` is ``True``.</span>

<span class="sd">    By default, this layer uses instance statistics computed from input data in</span>
<span class="sd">    both training and evaluation modes.</span>

<span class="sd">    If :attr:`track_running_stats` is set to ``True``, during training this</span>
<span class="sd">    layer keeps running estimates of its computed mean and variance, which are</span>
<span class="sd">    then used for normalization during evaluation. The running estimates are</span>
<span class="sd">    kept with a default :attr:`momentum` of 0.1.</span>

<span class="sd">    .. note::</span>
<span class="sd">        This :attr:`momentum` argument is different from one used in optimizer</span>
<span class="sd">        classes and the conventional notion of momentum. Mathematically, the</span>
<span class="sd">        update rule for running statistics here is</span>
<span class="sd">        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`,</span>
<span class="sd">        where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the</span>
<span class="sd">        new observed value.</span>

<span class="sd">    .. note::</span>
<span class="sd">        :class:`InstanceNorm3d` and :class:`LayerNorm` are very similar, but</span>
<span class="sd">        have some subtle differences. :class:`InstanceNorm3d` is applied</span>
<span class="sd">        on each channel of channeled data like 3D models with RGB color, but</span>
<span class="sd">        :class:`LayerNorm` is usually applied on entire sample and often in NLP</span>
<span class="sd">        tasks. Additionally, :class:`LayerNorm` applies elementwise affine</span>
<span class="sd">        transform, while :class:`InstanceNorm3d` usually don&#39;t apply affine</span>
<span class="sd">        transform.</span>

<span class="sd">    Args:</span>
<span class="sd">        num_features: :math:`C` from an expected input of size</span>
<span class="sd">            :math:`(N, C, D, H, W)`</span>
<span class="sd">        eps: a value added to the denominator for numerical stability. Default: 1e-5</span>
<span class="sd">        momentum: the value used for the running_mean and running_var computation. Default: 0.1</span>
<span class="sd">        affine: a boolean value that when set to ``True``, this module has</span>
<span class="sd">            learnable affine parameters, initialized the same way as done for batch normalization.</span>
<span class="sd">            Default: ``False``.</span>
<span class="sd">        track_running_stats: a boolean value that when set to ``True``, this</span>
<span class="sd">            module tracks the running mean and variance, and when set to ``False``,</span>
<span class="sd">            this module does not track such statistics and always uses batch</span>
<span class="sd">            statistics in both training and eval modes. Default: ``False``</span>

<span class="sd">    Shape:</span>
<span class="sd">        - Input: :math:`(N, C, D, H, W)`</span>
<span class="sd">        - Output: :math:`(N, C, D, H, W)` (same shape as input)</span>

<span class="sd">    Examples::</span>

<span class="sd">        &gt;&gt;&gt; # Without Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.InstanceNorm3d(100)</span>
<span class="sd">        &gt;&gt;&gt; # With Learnable Parameters</span>
<span class="sd">        &gt;&gt;&gt; m = nn.InstanceNorm3d(100, affine=True)</span>
<span class="sd">        &gt;&gt;&gt; input = torch.randn(20, 100, 35, 45, 10)</span>
<span class="sd">        &gt;&gt;&gt; output = m(input)</span>

<span class="sd">    .. _`Instance Normalization: The Missing Ingredient for Fast Stylization`:</span>
<span class="sd">        https://arxiv.org/abs/1607.08022</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">_check_input_dim</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">5</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected 5D input (got </span><span class="si">{}</span><span class="s1">D input)&#39;</span>
                             <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span></div>
</pre></div>

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